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首页 > 过刊浏览>2023年第51卷第3期 >405-415. DOI:10.11908/j.issn.0253-374x.22011
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基于集成学习的信号控制交叉口排队长度估计
CSTR:
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作者:
作者单位:

1.同济大学 道路与交通工程教育部重点实验室，上海 201804;2.中共兴平市委组织部，陕西 兴平 713100

作者简介:

吴 浩（1996—），男，博士生，主要研究方向为数据驱动的信号控制评估与优化。 E-mail：mas@tongji.edu.cn

通讯作者:

唐克双（1980—），男，教授，博士生导师，工学博士，主要研究方向为交通信号控制、智能交通。 E-mail：tang@tongji.edu.cn

中图分类号:

U491.4

基金项目:

国家自然科学基金（61673302）


Queue Length Estimation at Signalized Intersection Based on Ensemble Learning
Author:
Affiliation:

1.Key Laboratory of Road and Traffic Engineering of the Ministry of Education，Tongji University， Shanghai 201804， China;2.Organization Department of CPC Xingping Municipal Committee， Xingping 713100， China

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    摘要:

    基于电子警察（LPR）数据和网联车辆轨迹数据，提出了一种基于集成学习的信号控制交叉口排队长度估计方法。通过分析不同数据条件下估计方法的适用条件和精度水平，运用随机森林方法设计集成学习器，并构建电子警察和网联车辆轨迹感知信息及不同方法估计结果和真实排队长度之间的非线性映射关系。仿真结果表明：本方法的平均绝对误差为1.3 m?周期-1?车道-1，平均绝对百分比误差为1.4%。

    Abstract:

    Based on license plate recognition （LPR） data and connected vehicle trajectory data， an ensemble learning method was deployed to estimate the intersection queue length. By analyzing the applicability and accuracy of different queue length estimation methods， the random forest method was applied to design a base ensemble learner and formulize the nonlinear mapping relationship among the LPR data， connected vehicle trajectory data， estimation results of the existing queue length methods and real queue length values. Simulation results show that the proposed method overperforms the existing queue length methods， with a mean absolute error of 1.3 m?cycle-1?lane-1 and a mean absolute percent error of 1.4%.

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吴浩,刘磊,唐克双.基于集成学习的信号控制交叉口排队长度估计[J].同济大学学报(自然科学版),2023,51(3):405~415

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  • 收稿日期:2022-01-07
  • 在线发布日期: 2023-03-29
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